15 citations · 26 across the 11 of their papers we have counts for
5 papers · 1 filter
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Longfeng Wu, Bowen Lei, Dongkuan Xu +1
Rare categories abound in a number of real-world networks and play a pivotal role in a variety of high-stakes applications, including financial fraud detection, network intrusion d…
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
Shuaicheng Zhang, Haohui Wang, Si Zhang +1
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect…
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
Haohui Wang, Baoyu Jing, Kaize Ding +6
In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitig…
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
Haohui Wang, Yuzhen Mao, Yujun Yan +8
Non-IID transfer learning on graphs is crucial in many high-stakes domains. The majority of existing works assume stationary distribution for both source and target domains. Howeve…
FairGen: Towards Fair Graph Generation
Lecheng Zheng, Dawei Zhou, Hanghang Tong +3
There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks,…